A new reinforcement learning algorithm, Soft MellowMax Actor-Critic (SMM-AC), has been developed to improve the transfer of policies from simulation to real-world robots. This algorithm, based on the soft mellowmax value function, offers strong robustness guarantees. In tests on MuJoCo benchmarks, SMM-AC performed comparably to existing methods like Soft Actor-Critic. Crucially, on real robots, SMM-AC demonstrated superior return and greater resilience to variations in observations, the environment, and dynamics, highlighting the benefits of regularized RL for sim-to-real transfer. AI
IMPACT This research demonstrates that regularized reinforcement learning can improve the robustness of AI policies when transferring from simulated environments to real-world applications, potentially accelerating robotic deployments.
RANK_REASON The cluster contains a research paper detailing a new algorithm and its performance on benchmarks and real-world tests.
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